The recent progress of learning-based methods in computer vision for optical flow estimation have inspired new approaches for strain elastography [1]}. Those methods demonstrated the ability of neural networks to exploit the ultrasound high-frequency content and to robustly produce accurate displacement estimates [2]}, [3]}. Previous networks have been trained using ultrasound simulation associated with ground truth displacement and strain labels. Accurate ground-truth images can be difficult to obtain for quasi-static elastography because the magnitude of applied stress is unknown. However, real-world ultrasound data often exhibits complex speckle patterns and echogenic features that can be quite challenging to replicate in ultrasound simulation.
